U.S. patent number 10,884,398 [Application Number 16/240,466] was granted by the patent office on 2021-01-05 for systems and methods for prediction model update scheduling for building equipment.
This patent grant is currently assigned to Johnson Controls Technology Company. The grantee listed for this patent is Johnson Controls Technology Company. Invention is credited to Mohammad N. ElBsat, Michael J. Wenzel.
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United States Patent |
10,884,398 |
ElBsat , et al. |
January 5, 2021 |
Systems and methods for prediction model update scheduling for
building equipment
Abstract
A building system includes building equipment operable to
consume one or more resources and a control system configured to
generate, based on a prediction model, predictions of a load on the
building equipment or a price of the one or more resources for a
plurality of time steps in an optimization period, solve, based on
the predictions, an optimization problem to generate control inputs
for the equipment that minimize a predicted cost of consuming the
resources over the optimization period, control the building
equipment to operate in accordance with the control inputs, monitor
an error metric that characterizes an error between the predictions
and actual values of the at least one of the load on the building
equipment or the price of the one or more resources during the
optimization period, detect an occurrence of a trigger condition,
and in response to detecting the trigger condition, update the
prediction model.
Inventors: |
ElBsat; Mohammad N. (Milwaukee,
WI), Wenzel; Michael J. (Grafton, WI) |
Applicant: |
Name |
City |
State |
Country |
Type |
Johnson Controls Technology Company |
Auburn Hills |
MI |
US |
|
|
Assignee: |
Johnson Controls Technology
Company (Auburn Hills, MI)
|
Family
ID: |
1000005282892 |
Appl.
No.: |
16/240,466 |
Filed: |
January 4, 2019 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20200218233 A1 |
Jul 9, 2020 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05F
1/66 (20130101); G05B 19/418 (20130101); G05B
13/042 (20130101); G06Q 50/06 (20130101); G05B
13/048 (20130101); G05B 15/02 (20130101); G05B
2219/31414 (20130101); G05B 2219/32021 (20130101) |
Current International
Class: |
G05B
19/418 (20060101); G05F 1/66 (20060101); G05B
13/04 (20060101); G06Q 50/06 (20120101); G05B
15/02 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
US. Appl. No. 15/953,324, filed Apr. 13, 2018, Johnson Controls
Technology Company. cited by applicant .
U.S. Appl. No. 16/115,290, filed Aug. 28, 2018, Kumar, et al. cited
by applicant.
|
Primary Examiner: Lin; Jason
Attorney, Agent or Firm: Foley & Lardner LLP
Claims
What is claimed is:
1. A building system comprising: building equipment operable to
consume one or more resources and affect a variable state or
condition of a building, the resources provided by one or more
utility systems; a control system configured to: generate, based on
a prediction model, predictions of at least one of a load on the
building equipment or a price of the one or more resources for a
plurality of time steps in an optimization period; solve, based on
the predictions, an optimization problem to generate control inputs
for the equipment that minimize a predicted cost of consuming the
one or more resources over the optimization period; control the
building equipment to operate in accordance with the control
inputs; monitor an error metric that characterizes an error between
the predictions and actual values of the at least one of the load
on the building equipment or the price of the one or more resources
during the optimization period; update a control region for the
error metric in accordance with a change in a statistical property
of the error metric; detect an occurrence of a trigger condition by
comparing the error metric to the control region; and in response
to detecting the occurrence of the trigger condition, update the
prediction model to generate an updated prediction model; generate
updated control inputs for the building equipment based on the
updated prediction model; and control the building equipment to
operate in accordance with the updated control inputs.
2. The building system of claim 1, wherein the control system is
configured to calculate the error metric as a coefficient of
variation weighted mean absolute prediction error of the
predictions.
3. The building system of claim 2, wherein the control system is
configured to calculate the coefficient of variation weighted mean
absolute prediction error using a user-selectable weighting.
4. The building system of claim 1, wherein the control system is
configured to calculate the error metric as a smoothed current
prediction error.
5. The building system of claim 1, wherein the trigger condition
occurs when the error metric is outside the control region.
6. The building system of claim 5, wherein the trigger condition
occurs when the error metric is outside the control region for at
least a threshold duration.
7. The building system of claim 5, wherein the control system is
configured to determine at least one of an upper limit of the
control region or a lower limit of the control region as a based on
the statistical property of the error metric.
8. The building system of claim 7, wherein the control system is
configured to update the statistical property of the error metric
in response to updating the prediction model.
9. A method for controlling a building system, the method
comprising: operating building equipment to consume one or more
resources and affect a variable state or condition of a building,
the resources provided by one or more utility systems; generating,
based on a prediction model, predictions of at least one of a load
on the building equipment or a price of the one or more resources
for a plurality of time steps in a time period; generating, based
on the predictions, control inputs for the equipment that manage a
predicted cost of consuming the one or more resources over the time
period; controlling the building equipment to operate in accordance
with the control inputs; monitoring an error metric that
characterizes an error between the predictions and actual values of
the at least one of the load on the building equipment or the price
of the one or more resources during the time period; updating a
control region for the error metric in accordance with a change in
a statistical property of the error metric; detecting an occurrence
of a trigger condition by comparing the error metric to the control
region; in response to detecting the occurrence of the trigger
condition, updating the prediction model to generate an updated
prediction model; generating updated control inputs for the
building equipment based on the updated prediction model; and
controlling the building equipment to operate in accordance with
the updated control inputs.
10. The method of claim 9, comprising calculating the error metric
as a coefficient of variation weighted mean absolute prediction
error of the predictions.
11. The method of claim 10, comprising calculating the coefficient
of variation weighted mean absolute prediction error using a
user-selectable weighting.
12. The method of claim 9, comprising calculating the error metric
as a smoothed current prediction error.
13. The method of claim 9, wherein the trigger condition occurs
when the error metric is outside the control region.
14. The method of claim 13, wherein the trigger condition occurs
when the error metric is outside the control region for at least a
threshold duration.
15. The method of claim 13, comprising determining at least one of
an upper limit of the control region or a lower limit of the
control region based on the statistical property of the error
metric.
16. The method of claim 15, comprising the updating the statistical
property of the error metric in response to updating the prediction
model.
17. A central plant comprising: a plurality of subplants operable
to consume, generate, or store one or more resources, at least one
of the resources provided by a utility system at a utility rate; a
control system configured to: generate, based on one or more
prediction models, predictions of at least one of a load on a
campus served by the central plant or the utility rate for a
plurality of time steps in an optimization period; solve, based on
the predictions, an optimization problem to generate control inputs
for the subplants that minimize a predict cost of consuming the
resource provided by the utility system over the optimization
period; control the subplants to operate in accordance with the
control inputs; monitor an error metric that characterizes an error
between the predictions and actual values of the at least one of
the load on the campus served by the central plant or the utility
rate; detect an occurrence of a trigger condition, the trigger
condition dynamically updated in accordance with a time-varying
statistical property of the error metric; in response to detecting
the trigger condition, update the one or more prediction models;
control the plurality of subplants in accordance with an
optimization strategy generated based on the one or more prediction
models.
18. The central plant of claim 17, wherein the control system is
configured to calculate the error metric as a coefficient of
variation weighted mean absolute prediction error of the
predictions using a user-selectable weighting.
19. The central plant of claim 17, wherein the control system is
configured to calculate the error metric as a smoothed current
prediction error.
20. The central plant of claim 17, wherein the control system is
configured to generate a graphical user interface comprising a
visualization of the error metric and the trigger condition.
Description
BACKGROUND
The present disclosure relates generally to the field of building
equipment, and in particular to various types of building equipment
that may be controlled using prediction models. Building equipment
may include, among other possibilities, HVAC systems, airside
systems, waterside systems, variable refrigerant flow (VRF)
systems, central plant equipment, and/or other equipment operable
to affect a variable state or condition of a building (e.g.,
temperature, humidity, airflow, lighting, etc.).
Operators of building equipment may desire to control such building
equipment to optimize costs, for example to minimize the cost of a
utility consumed by the building equipment while maintaining
occupant comfort. These optimizations may be performed over future
optimization periods, for example to plan optimal control of
building equipment over the upcoming week or month. Accurate
prediction models used to predict system behavior over the
optimization period are therefore important to successful
optimization of building equipment control. Accordingly, systems
and methods for ensuring the ongoing accuracy of prediction models
are needed.
SUMMARY
One implementation of the present disclosure is a building system.
The building system includes building equipment operable to consume
one or more resources and affect a variable state or condition of a
building. The resources are provided by one or more utility
systems. The building system includes a control system configured
to generate, based on a prediction model, predictions of at least
one of a load on the building equipment or a price of the one or
more resources for a plurality of time steps in an optimization
period, solve, based on the predictions, an optimization problem to
generate control inputs for the equipment that minimize a predicted
cost of consuming the one or more resources over the optimization
period, control the building equipment to operate in accordance
with the control inputs, monitor an error metric that characterizes
an error between the predictions and actual values of the at least
one of the load on the building equipment or the price of the one
or more resources during the optimization period, and detect an
occurrence of a trigger condition. The trigger condition is defined
as a property of the error metric. The control system is also
configured to, in response to detecting the trigger condition,
update the prediction model to generate an updated prediction
model, generate updated control inputs for the building equipment
based on the updated prediction model, and control the building
equipment to operate in accordance with the updated control
inputs.
In some embodiments, the control system is configured to calculate
the error metric as a coefficient of variation weighted mean
absolute prediction error of the predictions. In some embodiments,
the control system is configured to calculate the coefficient of
variation weighted mean absolute prediction error using a
user-selectable weighting.
In some embodiments, the control system is configured to calculate
the error metric as a smoothed current prediction error. In some
embodiments, the trigger condition occurs when the error metric is
outside a control region.
In some embodiments, the trigger condition occurs when the error
metric is outside the control region for at least a threshold
duration. In some embodiments, the control system is configured to
determine at least one of an upper limit of the control region or a
lower limit of the control region as a statistical property of the
error metric. In some embodiments, the control system is configured
to updated the at least one of the upper limit of the control
region or the lower limit of the control region in accordance with
a change in the statistical property of the error metric over time.
In some embodiments, the control system is configured to update the
statistical property of the error metric in response to updating
the prediction model.
Another implementation of the present disclosure is a method for
controlling a building system. The method includes operating
building equipment to consume one or more resources and affect a
variable state or condition of a building. The resources are
provided by one or more utility systems. The method also includes
generating, based on a prediction model, predictions of at least
one of a load on the building equipment or a price of the one or
more resources for a plurality of time steps in a time period,
generating, based on the predictions, control inputs for the
equipment that manage a predicted cost of consuming the one or more
resources over the time period, controlling the building equipment
to operate in accordance with the control inputs, monitoring an
error metric that characterizes an error between the predictions
and actual values of the at least one of the load on the building
equipment or the price of the one or more resources during the time
period, and detecting an occurrence of a trigger condition. The
trigger condition is defined as a property of the error metric. The
method also includes, in response to detecting the trigger
condition, updating the prediction model to generate an updated
prediction model, generating updated control inputs for the
building equipment based on the updated prediction model, and
controlling the building equipment to operate in accordance with
the updated control inputs.
In some embodiments, the method includes calculating the error
metric as a coefficient of variation weighted mean absolute
prediction error of the predictions. In some embodiments, the
method includes calculating the coefficient of variation weighted
mean absolute prediction error using a user-selectable
weighting.
In some embodiments, the method includes calculating the error as a
smoothed current prediction error. In some embodiments, the trigger
condition occurs when the error is outside a control region. In
some embodiments, the trigger condition occurs when the error
metric is outside the control region for at least a threshold
duration. In some embodiments, the method includes determining at
least one of an upper limit of the control region or a lower limit
of the control region as a statistical property of the error
metric. In some embodiments, the method includes updating the at
least one of the upper limit of the control region or the lower
limit of the control region in accordance with a change in the
statistical property of the error metric over time. In some
embodiments, the method includes updating the statistical property
of the error metric in response to updating the prediction
model.
Another implementation of the present disclosure is a central
plant. The central plant includes a plurality of subplants operable
to consume, generate, or store one or more resources. At least one
of the resources is provided by a utility system at a utility rate.
The central plant also includes a control system configured to
generate, based on one or more prediction models, predictions of at
least one of a load on a campus served by the central plant or the
utility rate for a plurality of time steps in an optimization
period, solve, based on the predictions, an optimization problem to
generate control inputs for the subplants that minimize a predicted
cost of consuming the resource provided by the utility system over
the optimization period, control the subplants to operate in
accordance with the control inputs, monitor an error metric that
characterizes an error between the predictions and actual values of
the at least one of the load on the campus served by the central
plant or the utility rate, and detect an occurrence of a trigger
condition. The trigger condition defined as a property of the error
metric. The control system is also configured to, in response to
detecting the trigger condition, update the one or more prediction
models and control the plurality of subplants in accordance with an
optimization strategy generated based on the updated prediction
models.
In some embodiments, the control system is configured to calculate
the error metric as a coefficient of variation weighted mean
absolute prediction error of the predictions using a
user-selectable weighting. In some embodiments, the control system
is configured to calculate the error metric as a smoothed current
prediction error. In some embodiments, the control system is
configured to generate a graphical user interface that includes a
visualization of the error metric and the trigger condition.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a perspective view of a building served by a building
management system and an HVAC system, according to an exemplary
embodiment.
FIG. 2 is a first illustration of a variable refrigerant flow
system, according to an exemplary embodiment.
FIG. 3 is a second illustration of a variable refrigerant flow
system, according to an exemplary embodiment.
FIG. 4 is a block diagram of a variable refrigerant flow system,
according to an exemplary embodiment.
FIG. 5 is a block diagram of building system with a model
predictive controller, according to an exemplary embodiment.
FIG. 6 is a block diagram of a central plant with an energy storage
system is shown, according to an exemplary embodiment.
FIG. 7 is a block diagram of a control system for use with the
central plant of FIG. 6, according to an exemplary embodiment.
FIG. 8 is a block diagram of a prediction models system used with
building equipment, according to an exemplary embodiment.
FIG. 9 is a block diagram of an update scheduler circuit of the
prediction models system of FIG. 8, according to an exemplary
embodiment.
DETAILED DESCRIPTION
Building Equipment
Referring now to FIGS. 1-8, various types of building equipment are
shown, according to exemplary embodiments. Building equipment
refers to any equipment, device, etc. operable alone or in
combination with other equipment to affect a variable state or
condition of a building (e.g., temperature, airflow, humidity,
light, etc.), including by generating or storing energy for use by
other equipment (e.g., central plant equipment). The embodiments
shown and described herein include a heating, ventilation, and
cooling (HVAC) system as shown in FIG. 1, a variable refrigerant
flow (VRF) system as shown in FIG. 2-4, a building system with a
model predictive controller as shown in FIG. 5, and a central plant
as shown in FIG. 6-7. It should be understood that variations and
combinations of these systems and other building equipment are
within the scope of the present disclosure.
Building HVAC Systems
Referring to FIG. 1, a perspective view of a building 10 is shown,
according to an exemplary embodiment. Building 10 is served by a
building management system (BMS). A BMS is, in general, a system of
devices configured to control, monitor, and manage equipment in or
around a building or building area. A BMS can include, for example,
a HVAC system, a security system, a lighting system, a fire
alerting system, any other system that is capable of managing
building functions or devices, or any combination.
The BMS that serves building 10 includes a HVAC system 100. HVAC
system 100 can include a plurality of HVAC devices (e.g., heaters,
chillers, air handling units, pumps, fans, thermal energy storage,
etc.) configured to provide heating, cooling, ventilation, or other
services for building 10. For example, HVAC system 100 is shown to
include a waterside system 120 and an airside system 130. Waterside
system 120 may provide a heated or chilled fluid to an air handling
unit of airside system 130. Airside system 130 may use the heated
or chilled fluid to heat or cool an airflow provided to building
10.
HVAC system 100 is shown to include a chiller 102, a boiler 104,
and a rooftop air handling unit (AHU) 106. Waterside system 120 may
use boiler 104 and chiller 102 to heat or cool a working fluid
(e.g., water, glycol, etc.) and may circulate the working fluid to
AHU 106. In various embodiments, the HVAC devices of waterside
system 120 can be located in or around building 10 (as shown in
FIG. 1) or at an offsite location such as a central plant (e.g., a
chiller plant, a steam plant, a heat plant, etc.). The working
fluid can be heated in boiler 104 or cooled in chiller 102,
depending on whether heating or cooling is required in building 10.
Boiler 104 may add heat to the circulated fluid, for example, by
burning a combustible material (e.g., natural gas) or using an
electric heating element. Chiller 102 may place the circulated
fluid in a heat exchange relationship with another fluid (e.g., a
refrigerant) in a heat exchanger (e.g., an evaporator) to absorb
heat from the circulated fluid. The working fluid from chiller 102
and/or boiler 104 can be transported to AHU 106 via piping 108.
AHU 106 may place the working fluid in a heat exchange relationship
with an airflow passing through AHU 106 (e.g., via one or more
stages of cooling coils and/or heating coils). The airflow can be,
for example, outside air, return air from within building 10, or a
combination of both. AHU 106 may transfer heat between the airflow
and the working fluid to provide heating or cooling for the
airflow. For example, AHU 106 can include one or more fans or
blowers configured to pass the airflow over or through a heat
exchanger containing the working fluid. The working fluid may then
return to chiller 102 or boiler 104 via piping 110.
Airside system 130 may deliver the airflow supplied by AHU 106
(i.e., the supply airflow) to building 10 via air supply ducts 112
and may provide return air from building 10 to AHU 106 via air
return ducts 114. In some embodiments, airside system 130 includes
multiple variable air volume (VAV) units 116. For example, airside
system 130 is shown to include a separate VAV unit 116 on each
floor or zone of building 10. VAV units 116 can include dampers or
other flow control elements that can be operated to control an
amount of the supply airflow provided to individual zones of
building 10. In other embodiments, airside system 130 delivers the
supply airflow into one or more zones of building 10 (e.g., via
supply ducts 112) without using intermediate VAV units 116 or other
flow control elements. AHU 106 can include various sensors (e.g.,
temperature sensors, pressure sensors, etc.) configured to measure
attributes of the supply airflow. AHU 106 may receive input from
sensors located within AHU 106 and/or within the building zone and
may adjust the flow rate, temperature, or other attributes of the
supply airflow through AHU 106 to achieve setpoint conditions for
the building zone.
HVAC system 100 thereby provides heating and cooling to the
building 10. The building 10 also includes other sources of heat
transfer that the indoor air temperature in the building 10. The
building mass (e.g., walls, floors, furniture) influences the
indoor air temperature in building 10 by storing or transferring
heat (e.g., if the indoor air temperature is less than the
temperature of the building mass, heat transfers from the building
mass to the indoor air). People, electronic devices, other
appliances, etc. ("heat load") also contribute heat to the building
10 through body heat, electrical resistance, etc. Additionally, the
outside air temperature impacts the temperature in the building 10
by providing heat to or drawing heat from the building 10.
HVAC system 100 also includes a control system 150. The control
system 150 receives data about the HVAC system 100 and the building
10 (e.g., indoor air temperature, outdoor air temperatures, etc.)
and generates control signals to control the HVAC system 100 to
heat and/or cool the building 10. The control system 150 may be
configured to perform one or more optimizations to determine
optimal control for the HVAC system 100 over an optimization
period, for example as described with reference to FIGS. 5 and 8-9
below. Furthermore, the control system 150 may be configured to
perform online scheduling of prediction model updates as described
with reference to FIGS. 8-9.
Variable Refrigerant Flow System
Referring now to FIGS. 2-4, a variable refrigerant flow (VRF)
system 200 serving a building 201 is shown, according to exemplary
embodiments. VRF system 200 is shown to include one or more outdoor
VRF units 202 and a plurality of indoor VRF units 204. Outdoor VRF
units 202 can be located outside a building and can operate to heat
or cool a refrigerant. Outdoor VRF units 202 can consume
electricity to convert refrigerant between liquid, gas, and/or
super-heated gas phases. Indoor VRF units 204 can be distributed
throughout various building zones within a building and can receive
the heated or cooled refrigerant from outdoor VRF units 202. Each
indoor VRF unit 204 can provide temperature control for the
particular building zone in which the indoor VRF unit 204 is
located. Although the term "indoor" is used to denote that the
indoor VRF units 204 are typically located inside of buildings, in
some cases one or more indoor VRF units are located "outdoors"
(i.e., outside of a building) for example to heat/cool a patio,
entryway, walkway, etc.
One advantage of VRF system 200 is that some indoor VRF units 204
can operate in a cooling mode while other indoor VRF units 204
operate in a heating mode. For example, each of outdoor VRF units
202 and indoor VRF units 204 can operate in a heating mode, a
cooling mode, or an off mode. Each building zone can be controlled
independently and can have different temperature setpoints. In some
embodiments, each building has up to three outdoor VRF units 202
located outside the building (e.g., on a rooftop) and up to 128
indoor VRF units 204 distributed throughout the building (e.g., in
various building zones). Building zones may include, among other
possibilities, apartment units, offices, retail spaces, and common
areas. In some cases, various building zones are owned, leased, or
otherwise occupied by a variety of tenants, all served by the VRF
system 200.
Many different configurations exist for VRF system 200. In some
embodiments, VRF system 200 is a two-pipe system in which each
outdoor VRF unit 202 connects to a single refrigerant return line
and a single refrigerant outlet line. In a two-pipe system, all of
outdoor VRF units 202 may operate in the same mode since only one
of a heated or chilled refrigerant can be provided via the single
refrigerant outlet line. In other embodiments, VRF system 200 is a
three-pipe system in which each outdoor VRF unit 202 connects to a
refrigerant return line, a hot refrigerant outlet line, and a cold
refrigerant outlet line. In a three-pipe system, both heating and
cooling can be provided simultaneously via the dual refrigerant
outlet lines. An example of a three-pipe VRF system 200 is
described in detail with reference to FIG. 4.
In FIG. 4, the VRF system 200 is shown to include outdoor VRF unit
202, several heat recovery units 206, and several indoor VRF units
204. Outdoor VRF unit 202 may include a compressor 208, a fan 210,
or other power-consuming refrigeration components configured
convert a refrigerant between liquid, gas, and/or super-heated gas
phases. Indoor VRF units 204 can be distributed throughout various
building zones within a building and can receive the heated or
cooled refrigerant from outdoor VRF unit 202. Each indoor VRF unit
204 can provide temperature control for the particular building
zone in which the indoor VRF unit 204 is located. Heat recovery
units 206 can control the flow of a refrigerant between outdoor VRF
unit 202 and indoor VRF units 204 (e.g., by opening or closing
valves) and can minimize the heating or cooling load to be served
by outdoor VRF unit 202.
Outdoor VRF unit 202 is shown to include a compressor 208 and a
heat exchanger 212. Compressor 208 circulates a refrigerant between
heat exchanger 212 and indoor VRF units 204. The compressor 208
operates at a variable frequency as controlled by outdoor unit
controls circuit 214. At higher frequencies, the compressor 208
provides the indoor VRF units 204 with greater heat transfer
capacity. Electrical power consumption of compressor 208 increases
proportionally with compressor frequency.
Heat exchanger 212 can function as a condenser (allowing the
refrigerant to reject heat to the outside air) when VRF system 200
operates in a cooling mode or as an evaporator (allowing the
refrigerant to absorb heat from the outside air) when VRF system
200 operates in a heating mode. Fan 210 provides airflow through
heat exchanger 212. The speed of fan 210 can be adjusted (e.g., by
outdoor unit controls circuit 214) to modulate the rate of heat
transfer into or out of the refrigerant in heat exchanger 212.
Each indoor VRF unit 204 is shown to include a heat exchanger 216
and an expansion valve 218. Each of heat exchangers 216 can
function as a condenser (allowing the refrigerant to reject heat to
the air within the room or zone) when the indoor VRF unit 204
operates in a heating mode or as an evaporator (allowing the
refrigerant to absorb heat from the air within the room or zone)
when the indoor VRF unit 204 operates in a cooling mode. Fans 220
provide airflow through heat exchangers 216. The speeds of fans 220
can be adjusted (e.g., by indoor unit controls circuits 222) to
modulate the rate of heat transfer into or out of the refrigerant
in heat exchangers 216.
In FIG. 4, indoor VRF units 204 are shown operating in the cooling
mode. In the cooling mode, the refrigerant is provided to indoor
VRF units 204 via cooling line 224. The refrigerant is expanded by
expansion valves 218 to a cold, low pressure state and flows
through heat exchangers 216 (functioning as evaporators) to absorb
heat from the room or zone within the building. The heated
refrigerant then flows back to outdoor VRF unit 202 via return line
226 and is compressed by compressor 208 to a hot, high pressure
state. The compressed refrigerant flows through heat exchanger 212
(functioning as a condenser) and rejects heat to the outside air.
The cooled refrigerant can then be provided back to indoor VRF
units 204 via cooling line 224. In the cooling mode, flow control
valves 228 can be closed and expansion valve 230 can be completely
open.
In the heating mode, the refrigerant is provided to indoor VRF
units 204 in a hot state via heating line 232. The hot refrigerant
flows through heat exchangers 216 (functioning as condensers) and
rejects heat to the air within the room or zone of the building.
The refrigerant then flows back to outdoor VRF unit via cooling
line 224 (opposite the flow direction shown in FIG. 2). The
refrigerant can be expanded by expansion valve 230 to a colder,
lower pressure state. The expanded refrigerant flows through heat
exchanger 212 (functioning as an evaporator) and absorbs heat from
the outside air. The heated refrigerant can be compressed by
compressor 208 and provided back to indoor VRF units 204 via
heating line 232 in a hot, compressed state. In the heating mode,
flow control valves 228 can be completely open to allow the
refrigerant from compressor 208 to flow into heating line 232.
As shown in FIG. 4, each indoor VRF unit 204 includes an indoor
unit controls circuit 222. Indoor unit controls circuit 222
controls the operation of components of the indoor VRF unit 204,
including the fan 220 and the expansion valve 218, in response to a
building zone temperature setpoint or other request to provide
heating/cooling to the building zone, for example a temperature
setpoint generated by the model predictive control circuit 402 of
FIG. 5. For example, the indoor unit controls circuit 222 can
generate a signal to turn the fan 220 on and off Indoor unit
controls circuit 222 also determines a heat transfer capacity
required by the indoor VRF unit 204 and a frequency of compressor
208 that corresponds to that capacity. When the indoor unit
controls circuit 222 determines that the indoor VRF unit 204 must
provide heating or cooling of a certain capacity, the indoor unit
controls circuit 222 then generates and transmits a compressor
frequency request to the outdoor unit controls circuit 214
including the compressor frequency corresponding to the required
capacity.
Outdoor unit controls circuit 214 receives compressor frequency
requests from one or more indoor unit controls circuits 222 and
aggregates the requests, for example by summing the compressor
frequency requests into a compressor total frequency. In some
embodiments, the compressor frequency has an upper limit, such that
the compressor total frequency cannot exceed the upper limit. The
outdoor unit controls circuit 214 supplies the compressor total
frequency to the compressor, for example as an input frequency
given to a DC inverter compressor motor of the compressor. The
indoor unit controls circuits 222 and the outdoor unit controls
circuit 214 thereby combine to modulate the compressor frequency to
match heating/cooling demand. The outdoor unit controls circuit 214
may also generate signals to control valve positions of the flow
control valves 228 and expansion valve 230, a compressor power
setpoint, a refrigerant flow setpoint, a refrigerant pressure
setpoint (e.g., a differential pressure setpoint for the pressure
measured by pressure sensors 236), on/off commands, staging
commands, or other signals that affect the operation of compressor
208, as well as control signals provided to fan 210 including a fan
speed setpoint, a fan power setpoint, an airflow setpoint, on/off
commands, or other signals that affect the operation of fan
210.
Indoor unit controls circuits 222 and outdoor unit controls circuit
214 may store and/or provide a data history of one or more control
signals generated by or provided to the controls circuits 214, 222.
For example, indoor unit controls circuits 222 may store and/or
provide a log of generated compressor request frequencies, fan
on/off times, and indoor VRF unit 204 on/off times. Outdoor unit
controls circuit 214 may store and/or provide a log of compressor
request frequencies and/or compressor total frequencies and
compressor runtimes.
The VRF system 200 is shown as running on electrical power provided
by an energy grid 250 via an outdoor meter 252 and an indoor meter
254. According to various embodiments, the energy grid 250 is any
supply of electricity, for example an electrical grid maintained by
a utility company and supplied with power by one or more power
plants. The outdoor meter 252 measures the electrical power
consumption over time of the outdoor VRF unit 202, for example in
kilowatt-hours (kWh). The indoor meter 254 measures the electrical
power consumption over time of the indoor VRF units 204, for
example in kWh. The VRF system 200 incurs energy consumption costs
based on the metered electrical power consumption of the outdoor
meter 252 and/or the indoor meter 254, as billed by the utility
company that provides the electrical power. The price of electrical
power (e.g., dollars per kWh) may vary over time.
The VRF system 200 also includes a control system 150. The control
system 150 generates control signals that control the operation of
the VRF system 200 (e.g., by coordinating the indoor unit controls
circuits 222 and the outdoor unit controls circuit 214). The
control system 150 may use a model predictive control approach. The
control system 150 may be configured to perform one or more
optimizations to determine optimal control for the VRF system 200
over a optimization period, for example as described with reference
to FIGS. 5 and 8-9 below. Furthermore, the control system 150 may
be configured to perform online scheduling of prediction model
updates as described with reference to FIGS. 8-9.
Building System with Model Predictive Controller
Referring now to FIG. 5, a building system 400 with a model
predictive controller 402 is shown, according to an exemplary
embodiment. The building system 400 includes the model predictive
controller 402 communicably and operably coupled to an equipment
controller 404. The equipment controller 404 is communicably and
operably coupled to equipment 406, which is operable to affect a
variable state or condition of a building 408, and to sensors 410
located in or near the building. The model predictive controller
402 is also communicably coupled to a weather service 412, a
utility system 414, and a system identification circuit 416.
The model predictive control circuit 402 is configured to generate
a temperature setpoint for the building for each time step in an
optimization period to optimize a cost of operating the equipment.
The model predictive control circuit 402 may be configured to use
weather forecasts from the weather service 412 and utility rate
data from the utility system 414 and a system model from the system
identification circuit 416 to generate predicted loads and rates
over a prediction horizon. The model predictive control circuit 402
may generate a cost function based on the predicted loads and rates
and optimize the cost function to generates temperature setpoints
for the optimization period. Various cost functions and model
predictive control processes are possible, for example as described
in U.S. patent application Ser. No. 16/115,290 filed Aug. 28, 2018,
incorporated by reference in its entirety herein. Various
configurations of the system identification circuit 416 are also
possible in various embodiments, for example as described in detail
in U.S. patent application Ser. No. 15/953,324, filed Apr. 13,
2018, incorporated by reference herein in its entirety.
The equipment controller 404 is configured to receive temperature
setpoints from the model predictive control circuit 402 and, in
response, control the equipment 406 to drive the temperature of the
building (e.g., the indoor air temperature Tia) towards the
temperature setpoint T.sub.sp. The equipment controller 404 may
include a proportional-integral-derivative (PID) controller or
proportional-integral (PI) controller. The equipment controller 404
provides a control input to the building equipment 406 (e.g.,
operating frequency, power level, valve positions, etc.), which
causes the building equipment 406 to operate to affect the
temperature or other environmental conditional of the building 408.
The sensors 410 measure the temperature (e.g., indoor air
temperature ET.sub.ia) or other environmental condition and provide
that measured data to the equipment controller 404. The sensors 410
may also provide other relevant data, for example an outside air
temperature T.sub.oa. The equipment controller 404, the equipment
406, and the sensors 410 thereby form a control loop configured to
drive the temperature in the building 408 towards the setpoint
T.sub.sp provided by the model predictive control circuit 402 for a
given time step.
The effectiveness of the model predictive controller in optimizing
a cost of operating the equipment 406 is therefore dependent on the
accuracy of the system model generated by the system identification
circuit 416 and/or other models used by the model predictive
controller 402 (e.g., a model used to predict future utility
rates). The accuracy of these models may decay over time, resulting
in sub-optimal performance of the building system 400. Accordingly,
systems and methods for scheduling model updates may be included
with the building system 400, for example as shown in FIGS. 8-9 and
described in detail with reference thereto.
Central Plant with Energy Storage System
Referring now to FIG. 6, a block diagram of an energy storage
system 500 is shown, according to an exemplary embodiment. Energy
storage system 500 is shown to include a building 502. Building 502
may be the same or similar to building 10, building 201, or
building 408, as described with reference to FIG. 1-5. For example,
building 502 may be equipped with a HVAC system, VRF system, and/or
a building management system that operates to control conditions
within building 502. In some embodiments, building 502 includes
multiple buildings (i.e., a campus) served by energy storage system
500. Building 502 may demand various resources including, for
example, hot thermal energy (e.g., hot water), cold thermal energy
(e.g., cold water), and/or electrical energy. The resources may be
demanded by equipment or subsystems within building 502 or by
external systems that provide services for building 502 (e.g.,
heating, cooling, air circulation, lighting, electricity, etc.).
Energy storage system 500 operates to satisfy the resource demand
associated with building 502.
Energy storage system 500 is shown to include a plurality of
utilities 510. Utilities 510 may provide energy storage system 500
with resources such as electricity, water, natural gas, or any
other resource that can be used by energy storage system 500 to
satisfy the demand of building 502. For example, utilities 510 are
shown to include an electric utility 511, a water utility 512, a
natural gas utility 513, and utility M 514, where M is the total
number of utilities 510. In some embodiments, utilities 510 are
commodity suppliers from which resources and other types of
commodities can be purchased. Resources purchased from utilities
510 can be used by generator subplants 520 to produce generated
resources (e.g., hot water, cold water, electricity, steam, etc.),
stored in storage subplants 530 for later use, or provided directly
to building 502. For example, utilities 510 are shown providing
electricity directly to building 502 and storage subplants 530.
Energy storage system 500 is shown to include a plurality of
generator subplants 520. In some embodiments, generator subplants
520 are components of a central plant. Generator subplants 520 are
shown to include a heater subplant 521, a chiller subplant 522, a
heat recovery chiller subplant 523, a steam subplant 524, an
electricity subplant 525, and subplant N, where N is the total
number of generator subplants 520. Generator subplants 520 may be
configured to convert one or more input resources into one or more
output resources by operation of the equipment within generator
subplants 520. For example, heater subplant 521 may be configured
to generate hot thermal energy (e.g., hot water) by heating water
using electricity or natural gas. Chiller subplant 522 may be
configured to generate cold thermal energy (e.g., cold water) by
chilling water using electricity. Heat recovery chiller subplant
523 may be configured to generate hot thermal energy and cold
thermal energy by removing heat from one water supply and adding
the heat to another water supply. Steam subplant 524 may be
configured to generate steam by boiling water using electricity or
natural gas. Electricity subplant 525 may be configured to generate
electricity using mechanical generators (e.g., a steam turbine, a
gas-powered generator, etc.) or other types of
electricity-generating equipment (e.g., photovoltaic equipment,
hydroelectric equipment, etc.).
The input resources used by generator subplants 520 may be provided
by utilities 510, retrieved from storage subplants 530, and/or
generated by other generator subplants 520. For example, steam
subplant 524 may produce steam as an output resource. Electricity
subplant 525 may include a steam turbine that uses the steam
generated by steam subplant 524 as an input resource to generate
electricity. The output resources produced by generator subplants
520 may be stored in storage subplants 530, provided to building
502, sold to energy purchasers 504, and/or used by other generator
subplants 520. For example, the electricity generated by
electricity subplant 525 may be stored in electrical energy storage
533, used by chiller subplant 522 to generate cold thermal energy,
provided to building 502, and/or sold to energy purchasers 504.
Energy storage system 500 is shown to include storage subplants
530. In some embodiments, storage subplants 530 are components of a
central plant. Storage subplants 530 may be configured to store
energy and other types of resources for later use. Each of storage
subplants 530 may be configured to store a different type of
resource. For example, storage subplants 530 are shown to include
hot thermal energy storage 531 (e.g., one or more hot water storage
tanks), cold thermal energy storage 532 (e.g., one or more cold
thermal energy storage tanks), electrical energy storage 533 (e.g.,
one or more batteries), and resource type P storage 534, where P is
the total number of storage subplants 530. The resources stored in
subplants 530 may be purchased directly from utilities 510 or
generated by generator subplants 520.
In some embodiments, storage subplants 530 are used by energy
storage system 500 to take advantage of price-based demand response
(PBDR) programs. PBDR programs encourage consumers to reduce
consumption when generation, transmission, and distribution costs
are high. PBDR programs are typically implemented (e.g., by
utilities 510) in the form of energy prices that vary as a function
of time. For example, utilities 510 may increase the price per unit
of electricity during peak usage hours to encourage customers to
reduce electricity consumption during peak times. Some utilities
also charge consumers a separate demand charge based on the maximum
rate of electricity consumption at any time during a predetermined
demand charge period.
Advantageously, storing energy and other types of resources in
subplants 530 allows for the resources to be purchased at times
when the resources are relatively less expensive (e.g., during
non-peak electricity hours) and stored for use at times when the
resources are relatively more expensive (e.g., during peak
electricity hours). Storing resources in subplants 530 also allows
the resource demand of building 502 to be shifted in time. For
example, resources can be purchased from utilities 510 at times
when the demand for heating or cooling is low and immediately
converted into hot or cold thermal energy by generator subplants
520. The thermal energy can be stored in storage subplants 530 and
retrieved at times when the demand for heating or cooling is high.
This allows energy storage system 500 to smooth the resource demand
of building 502 and reduces the maximum required capacity of
generator subplants 520. Smoothing the demand also allows energy
storage system 500 to reduce the peak electricity consumption,
which results in a lower demand charge.
In some embodiments, storage subplants 530 are used by energy
storage system 500 to take advantage of incentive-based demand
response (IBDR) programs. IBDR programs provide incentives to
customers who have the capability to store energy, generate energy,
or curtail energy usage upon request. Incentives are typically
provided in the form of monetary revenue paid by utilities 510 or
by an independent service operator (ISO). IBDR programs supplement
traditional utility-owned generation, transmission, and
distribution assets with additional options for modifying demand
load curves. For example, stored energy can be sold to energy
purchasers 504 (e.g., an energy grid) to supplement the energy
generated by utilities 510. In some instances, incentives for
participating in an IBDR program vary based on how quickly a system
can respond to a request to change power output/consumption. Faster
responses may be compensated at a higher level. Advantageously,
electrical energy storage 533 allows system 500 to quickly respond
to a request for electric power by rapidly discharging stored
electrical energy to energy purchasers 504.
Still referring to FIG. 6, energy storage system 500 is shown to
include control system 150. In the embodiment of FIG. 6, control
system 150 may be configured to control the distribution,
production, storage, and usage of resources in energy storage
system 500. In some embodiments, control system 150 performs an
optimization process to determine an optimal set of control
decisions for each time step within an optimization period. FIG. 7
shows a possible embodiment of such a control system 150. The
control decisions may include, for example, an optimal amount of
each resource to purchase from utilities 510, an optimal amount of
each resource to produce or convert using generator subplants 520,
an optimal amount of each resource to store or remove from storage
subplants 530, an optimal amount of each resource to sell to energy
purchasers 504, and/or an optimal amount of each resource to
provide to building 502. In some embodiments, the control decisions
include an optimal amount of each input resource and output
resource for each of generator subplants 520.
Control system 150 may be configured to maximize the economic value
of operating energy storage system 500 over the duration of the
optimization period. The economic value may be defined by a value
function that expresses economic value as a function of the control
decisions made by control system 150. The value function may
account for the cost of resources purchased from utilities 510,
revenue generated by selling resources to energy purchasers 504,
and the cost of operating energy storage system 500. In some
embodiments, the cost of operating energy storage system 500
includes a cost for losses in battery capacity as a result of the
charging and discharging electrical energy storage 533. The cost of
operating energy storage system 500 may also include a cost of
excessive equipment start/stops during the optimization period.
Each of subplants 520-534 may include equipment that can be
controlled by control system 150 to optimize the performance of
energy storage system 500. Subplant equipment may include, for
example, heating devices, chillers, heat recovery heat exchangers,
cooling towers, energy storage devices, pumps, valves, and/or other
devices of subplants 520-534. Individual devices of generator
subplants 520 can be turned on or off to adjust the resource
production of each generator subplant. In some embodiments,
individual devices of generator subplants 520 can be operated at
variable capacities (e.g., operating a chiller at 10% capacity or
60% capacity) according to an operating setpoint received from
control system 150.
In some embodiments, one or more of subplants 520-534 includes a
subplant level controller configured to control the equipment of
the corresponding subplant. For example, control system 150 may
determine an on/off configuration and global operating setpoints
for the subplant equipment. In response to the on/off configuration
and received global operating setpoints, the subplant controllers
may turn individual devices of their respective equipment on or
off, and implement specific operating setpoints (e.g., damper
position, vane position, fan speed, pump speed, etc.) to reach or
maintain the global operating setpoints.
In some embodiments, control system 150 maximizes the life cycle
economic value of energy storage system 500 while participating in
PBDR programs, IBDR programs, or simultaneously in both PBDR and
IBDR programs. For the IBDR programs, control system 150 may use
statistical estimates of past clearing prices, mileage ratios, and
event probabilities to determine the revenue generation potential
of selling stored energy to energy purchasers 504. For the PBDR
programs, control system 150 may use predictions of ambient
conditions, facility thermal loads, and thermodynamic models of
installed equipment to estimate the resource consumption of
subplants 520. Control system 150 may use predictions of the
resource consumption to monetize the costs of running the
equipment.
Control system 150 may automatically determine (e.g., without human
intervention) a combination of PBDR and/or IBDR programs in which
to participate over the optimization period in order to maximize
economic value. For example, control system 150 may consider the
revenue generation potential of IBDR programs, the cost reduction
potential of PBDR programs, and the equipment
maintenance/replacement costs that would result from participating
in various combinations of the IBDR programs and PBDR programs.
Control system 150 may weigh the benefits of participation against
the costs of participation to determine an optimal combination of
programs in which to participate. Advantageously, this allows
control system 150 to determine an optimal set of control decisions
that maximize the overall value of operating energy storage system
500.
In some instances, control system 150 may determine that it would
be beneficial to participate in an IBDR program when the revenue
generation potential is high and/or the costs of participating are
low. For example, control system 150 may receive notice of a
synchronous reserve event from an IBDR program which requires
energy storage system 500 to shed a predetermined amount of power.
Control system 150 may determine that it is optimal to participate
in the IBDR program if cold thermal energy storage 532 has enough
capacity to provide cooling for building 502 while the load on
chiller subplant 522 is reduced in order to shed the predetermined
amount of power.
In other instances, control system 150 may determine that it would
not be beneficial to participate in an IBDR program when the
resources required to participate are better allocated elsewhere.
For example, if building 502 is close to setting a new peak demand
that would greatly increase the PBDR costs, control system 150 may
determine that only a small portion of the electrical energy stored
in electrical energy storage 533 will be sold to energy purchasers
504 in order to participate in a frequency response market. Control
system 150 may determine that the remainder of the electrical
energy will be used to power chiller subplant 522 to prevent a new
peak demand from being set.
The control system 150 may utilize a model predictive control
approach to fulfill these and other functions, including a
stochastic model predictive control approach as described in detail
in U.S. patent application Ser. No. 15/963,891, filed Apr. 26,
2018, incorporated by reference herein in its entirety.
Furthermore, the control system 150 may be configured to perform
online scheduling of prediction model updates as described with
reference to FIGS. 8-9.
Referring now to FIG. 7, a block diagram illustrating a portion of
central plant system 100 in greater detail is shown, according to
an exemplary embodiment. FIG. 7 illustrates a cascaded optimization
process performed by the control system 150 to optimize the
performance of central plant equipment 700 (e.g., generator
subplants 520 and storage subplants 530). In the cascaded
optimization process, a high level optimization circuit 730
performs a subplant level optimization that determines an optimal
distribution of thermal energy loads across generator subplants 520
and storage subplants 530 (shown in FIG. 7 as central plant
equipment 700) for each time step in a prediction window in order
to minimize the cost of energy consumed by central plant equipment
700. Low level optimization circuit 732 performs an equipment level
optimization that determines how to best run each subplant at the
subplant load setpoint determined by high level optimization
circuit 730. For example, low level optimization circuit 732 may
determine on/off states and/or operating setpoints for various
devices of central plant equipment 700 within the subplants 520,
530 in order to optimize the energy consumption of each subplant
while meeting the thermal energy load setpoint for the subplant.
The high level optimization circuit 730 and the low level
optimization circuit 732 may be included in the control system
150.
One advantage of the cascaded optimization process performed by
control circuit 150 is the optimal use of computational time. For
example, the subplant level optimization performed by high level
optimization circuit 730 may use a relatively long time horizon due
to the operation of the thermal energy storage. However, the
equipment level optimization performed by low level optimization
circuit 732 may use a much shorter time horizon or no time horizon
at all since the low level system dynamics are relatively fast
(compared to the dynamics of the thermal energy storage) and the
low level control of equipment may be handled by a building
automation system 708. Such an optimal use of computational time
makes it possible for the control system 150 to perform the central
plant optimization in a short amount of time, allowing for
real-time predictive control. For example, the short computational
time enables the control system 150 to be implemented in a
real-time planning tool with interactive feedback.
Another advantage of the cascaded optimization performed by the
control system 150 is that the central plant optimization problem
can be split into two cascaded subproblems. The cascaded
configuration provides a layer of abstraction that allows high
level optimization circuit 730 to distribute the thermal energy
loads across subplants without requiring high level optimization
circuit 730 to know or use any details regarding the particular
equipment configuration within each subplant. The interconnections
between equipment within each subplant may be hidden from high
level optimization circuit 730 and handled by low level
optimization circuit 732. For purposes of the subplant level
optimization performed by high level optimization circuit 730, each
subplant may be completely defined by one or more subplant
curves.
Still referring to FIG. 7, low level optimization circuit 732 may
generate and provide subplant curves to high level optimization
circuit 730. Subplant curves may indicate the rate of utility use
by each of subplants (e.g., electricity use measured in kW, water
use measured in L/s, etc.) as a function of the subplant load. In
some embodiments, low level optimization circuit 732 generates
subplant curves based on equipment models (e.g., by combining
equipment models for individual devices into an aggregate curve for
the subplant). Low level optimization circuit 732 may generate
subplant curves by running a low level optimization process for
several different loads and weather conditions to generate multiple
data points. Low level optimization circuit 732 may fit a curve to
the data points to generate subplant curves. In other embodiments,
low level optimization circuit 732 provides the data points to high
level optimization circuit 732 and high level optimization circuit
732 generates the subplant curves using the data points.
High level optimization circuit 730 may receive load and rate
predictions from a load/rate prediction circuit 722. The load/rate
predictions circuit 722 may generate load predictions based on
weather forecasts from weather service 412 and/or information from
building automation system 708 (e.g., a current electric load of
the building, measurements from the building, a history of previous
loads, a setpoint trajectory, etc.). The utility rate predictions
may be based on utility rates received from utility systems 414
and/or utility prices from another data source. The load/rate
predictions circuit 722 may use a predictive model to generate the
load/rate predictions. The predictive model may be identified using
historical data (e.g., historical predictions, historical loads,
historical weather, historical rates).
High level optimization circuit 730 may determine the optimal load
distribution for the subplants (e.g., a subplant load for each
subplant) for each time step the prediction window and provide the
subplant loads as setpoints to low level optimization circuit 732.
In some embodiments, high level optimization circuit 730 determines
the subplant loads by minimizing the total operating cost of
central plant equipment 700 over the prediction window. In other
words, given a predicted load and utility rate information from
load/rate prediction circuit 722, high level optimization circuit
730 may distribute the predicted load across subplants 521-526 over
the optimization period to minimize operating cost.
In some instances, the optimal load distribution may include using
storage subplants 530 to store resources during a first time step
for use during a later time step. For example, thermal storage may
advantageously allow thermal energy to be produced and stored
during a first time period when energy prices are relatively low
and subsequently retrieved and used during a second time period
when energy proves are relatively high. The high level optimization
may be described by the following equation:
.theta..times..times..theta..times..times..times..function..theta..times.
##EQU00001## where .theta.*.sub.HL contains the optimal high level
decisions (e.g., the optimal load for each of subplants 12-22) for
the entire optimization period and J.sub.HL is the high level cost
function.
To find the optimal high level decisions .theta.*.sub.HL, high
level optimization circuit 732 may minimize the high level cost
function J.sub.HL. The high level cost function J.sub.HL may be the
sum of the economic costs of each utility consumed by each of
subplants 520-534 for the duration of the optimization period. In
some embodiments, the high level cost function J.sub.HL may be
described using the following equation:
.function..theta..times..times..times..times..times..times..times..times.-
.function..theta..times. ##EQU00002## where n.sub.h is the number
of time steps k in the optimization period, n.sub.s is the number
of subplants, t.sub.s is the duration of a time step, c.sub.jk is
the economic cost of utility j at a time step k of the optimization
period, and u.sub.jik is the rate of use of utility j by subplant i
at time step k.
In some embodiments, the cost function J.sub.HL includes an
additional demand charge term such as:
.times..times..times..times..times..times..times..times..times..times..ti-
mes..function..theta..times. ##EQU00003## where w.sub.d is a
weighting term, c.sub.demand is the demand cost, and the max( )
term selects the peak electricity use during the applicable demand
charge period. Accordingly, the high level cost function J.sub.HL
may be described by the equation:
.function..theta..times..times..times..times..times..times..times..times.-
.function..theta..times..times..times..times..function..theta.
##EQU00004##
The decision vector .theta..sub.HL may be subject to several
constraints. For example, the constraints may require that the
subplants not operate at more than their total capacity, that the
thermal storage not charge or discharge too quickly or under/over
flow for the tank, and that the thermal energy loads for the
building or campus are met. These restrictions lead to both
equality and inequality constraints on the high level optimization
problem.
Still referring to FIG. 7, low level optimization circuit 732 may
use the subplant loads determined by high level optimization
circuit 730 to determine optimal low level decisions
.theta.*.sub.LL (e.g. binary on/off decisions, flow setpoints,
temperature setpoints, etc.) for equipment 700. The low level
optimization process may be performed for each of subplants 521-526
and 531-534. Low level optimization circuit 732 may be responsible
for determining which devices of each subplant to use and/or the
operating setpoints for such devices that will achieve the subplant
load setpoint while minimizing energy consumption. The low level
optimization may be described using the following equation:
.theta..times..theta..times..times..times..function..theta..times.
##EQU00005## where .theta.*.sub.LL contains the optimal low level
decisions and I.sub.LL is the low level cost function.
To find the optimal low level decisions .theta.*.sub.LL, low level
optimization circuit 732 may minimize the low level cost function
J.sub.LL. The low level cost function J.sub.LL may represent the
total energy consumption for all of equipment 700 in the applicable
subplant. The low level cost function J.sub.LL may be described
using the following equation:
.times..theta..times..times..function..theta..times. ##EQU00006##
where N is the number of devices of equipment in the subplant,
t.sub.s is the duration of a time step, b.sub.j is a binary on/off
decision (e.g., 0=off, 1=on), and u.sub.j is the energy used by
device j as a function of the setpoint .theta..sub.LL. Each device
may have continuous variables which can be changed to determine the
lowest possible energy consumption for the overall input
conditions.
Low level optimization circuit 732 may minimize the low level cost
function J.sub.LL subject to inequality constraints based on the
capacities of equipment and equality constraints based on energy
and mass balances. In some embodiments, the optimal low level
decisions .theta.*.sub.LL are constrained by switching constraints
defining a short horizon for maintaining a device in an on or off
state after a binary on/off switch. The switching constraints may
prevent devices from being rapidly cycled on and off. In some
embodiments, low level optimization circuit 732 performs the
equipment level optimization without considering system dynamics.
The optimization process may be slow enough to safely assume that
the equipment control has reached its steady-state. Thus, low level
optimization circuit 732 may determine the optimal low level
decisions .theta.*.sub.LL at an instance of time rather than over a
long horizon.
Low level optimization circuit 732 may determine optimum operating
statuses (e.g., on or off) for a plurality of devices of equipment
700. According to an exemplary embodiment, the on/off combinations
may be determined using binary optimization and quadratic
compensation. Binary optimization may minimize a cost function
representing the power consumption of devices in the applicable
subplant. In some embodiments, non-exhaustive (i.e., not all
potential combinations of devices are considered) binary
optimization is used. Quadratic compensation may be used in
considering devices whose power consumption is quadratic (and not
linear). Low level optimization circuit 732 may also determine
optimum operating setpoints for equipment using nonlinear
optimization. Nonlinear optimization may identify operating
setpoints that further minimize the low level cost function
J.sub.LL. Low level optimization circuit 732 may provide the on/off
decisions and setpoints to building automation system 708 for use
in controlling the central plant equipment 700.
In some embodiments, the low level optimization performed by low
level optimization circuit 732 is the same or similar to the low
level optimization process described in U.S. patent application
Ser. No. 14/634,615, filed Feb. 27, 2015, incorporated by reference
herein in its entirety.
In the optimization process illustrated by FIG. 7, the
effectiveness of the high level optimization and low level
optimization in achieving optimal or near-optimal costs is highly
dependent on the accuracy of the load and rate predictions. The
load and rate predictions may be generated using a predictive model
generated based on historical data, including historical
predictions, historical weather data, historical rates, and
historical loads. Because of changes in weather, seasons, loads,
behavior in the buildings, etc., the accuracy of the predictive
model typically decreases over time. The predictive model must
therefore be periodically be updated. In some approaches, the
predictive model may be updated on an ad hoc basis (e.g., in
response to user requests to update the model) or on a preset
schedule (e.g., weekly). As described in detail below, the present
disclosure relates to systems and methods for scheduling updates to
the predictive model based on the performance of the model.
Systems and Methods for Prediction Model Update Scheduling
Referring now to FIG. 8, a block diagram of a control system 150
configured to automatically schedule model updates is shown,
according to an exemplary embodiment. As shown in FIG. 8, the
control system 150 includes an update scheduler circuit 800, a
models update circuit 802, one or more prediction circuit(s) 804,
one or more optimization circuit(s) 806, one or more equipment
controller(s) 808, and a database 810, all communicably and
operably coupled to one another. The control system 150 is
communicably and operably coupled to equipment 812, one or more
meters 814, and one or more utility systems 414. It should be
understood that FIG. 8 shows one example embodiment and that
various other configurations of circuitry configured to execute the
functions described herein may be used in various embodiments.
The database 810 is configured to store both actual data relating
to loads, rates, system behavior, weather, etc. (i.e., measured
historical data) in addition to historical predictions (e.g.,
predictions of loads, rates, etc. made at a past point in time) and
current predictions of loads, rates, etc. The database 810 thereby
stores both the predicted and actual value of a load, rate, or
other metric for each timestep up to the present time. As shown in
FIG. 8, the database 810 receives predictions from the prediction
circuit(s) 804, actual loads from the meter(s) 814, actual rates
from the utility system(s) 414, and actual weather and weather
forecasts from the weather service 412. The database 810 may be
accessed by the update scheduler circuit 800, the models update
circuit 802 and/or other circuits (e.g., the optimization
circuit(s) 806) to allow use of the data stored therein to
facilitate the functions described below.
The update scheduler circuit 800 is configured to receive
historical predictions and actual data from the database 810,
calculate an error metric based on the historical predictions and
actual data, and determine when to update one or more prediction
models based on properties of the error metric. The update schedule
circuit 800 may provide an update command to the models update
circuit 802 in response to determining that the one or more
prediction models should be updated (e.g., in response to
determining that a property of the error metric satisfies a
predefined condition). The update scheduler circuit 800 is shown in
detail in FIG. 9 and described further with reference thereto.
The models update circuit 802 is configured to receive the update
command from the update scheduler circuit 800 and, in response,
update one or more models. The models update circuit 802 may
receive actual data (e.g., loads, rates, weather) from the database
810 and use the actual data in generating updated models. In
various embodiments, the models update circuit 802 may follow
various approaches to updating a model. For example, in some
embodiments the models update circuit 802 corresponds to the system
identification circuit 416 of FIG. 5, in which case the models
update circuit 802 may use a prediction error method as described
in U.S. patent application Ser. No. 15/953,324, filed Apr. 13,
2018, incorporated by reference herein in its entirety. In other
embodiments, the models update circuit 802 updates a prediction
model used for rate and load prediction by the load/rate prediction
circuit 722, for example as described in U.S. patent application
Ser. No. 14/717,593, filed May 20, 2015, incorporated by reference
herein in its entirety.
The models update circuit 302 provides the updated model(s) to one
or more prediction circuit(s) 804. In some embodiments, the
prediction circuit 804 is the load/rate prediction circuit 722 of
FIG. 7. In some embodiments, the prediction circuit 804 is included
in the model predictive control circuit 402 of FIG. 5. The
prediction circuit 804 generates predictions, i.e., future values
of one or more points characterizing at least one of loads, rates,
system behavior, etc. at each time step over a prediction horizon.
As shown in FIG. 8, the prediction circuit 804 may provide the
predictions to the database 810 and the one or more optimization
circuit(s) 806.
The optimization circuit(s) 806 is configured to optimize a cost
function over an optimization period using the predictions from the
prediction circuit 804 to generate setpoints, asset allocations,
subplant loads, or other optimization strategy for the equipment
812 over the optimization period. In some embodiments, the
optimization circuit(s) 806 includes the high level optimization
circuit 730 and/or the low level optimization circuit 732 of FIG.
7. In some embodiments, the optimization circuit 806 is included in
the model predictive control circuit 402 of FIG. 5. Various
optimizations are possible in various embodiments. The optimization
circuit 806 provides the setpoints, asset allocations, etc. to
equipment controllers 808. The equipment controllers 808 are
configured to generate control signals for the equipment 812 to
control the equipment 812 in accordance with the optimization
strategy determined by the optimization circuit(s) 806. For
example, the equipment controllers 808 may include the equipment
controller 404 of FIG. 5 and/or the building automation system 708
of FIG. 7.
The equipment 812 is controllable by the equipment controller(s)
808 to consume or store one or more resources from the utility
system(s) to affect one or more variable states or conditions of a
building or campus. The equipment 812 may include equipment 406 of
FIG. 5, central plant equipment 700 of FIG. 7, generator subplants
520 of FIG. 6, storage subplants 530 of FIG. 6, HVAC system 100 of
FIG. 1, and/or VRF system 200 of FIGS. 2-4, among other
possibilities. The one or more meter(s) 814 measure the amount of
the resource(s) consumed by the equipment 812 and provide the
measured load to the database 810. The utility system(s) 414 are
configured to provide the actual rates for the consumed load to the
database 810.
Referring now to FIG. 9, a detailed view of the update scheduler
circuit 800 is shown, according to an exemplary embodiment. As
shown in FIG. 9, the update scheduler circuit 800 is communicably
and operably coupled to the database 810 and a user device 900. The
user device 900 may include a personal computing device (e.g.,
desktop computer, laptop computer, tablet, smartphone,
augmented/virtual reality headset). The user device 900 is
configured to receive input from a user of various preferences
relating to operation of the update scheduler circuit 800 as
described in detail below. The user device 900 may also allow a
user to input a request to update a prediction model on demand.
As shown in FIG. 9, the update scheduler circuit 800 includes a
preferences circuit 902, an error metric calculation circuit 904, a
metric properties circuit 906, and a condition monitoring circuit
908. The preferences circuit 902 is configured to store various
preferences (settings) relating to the operation of the update
scheduler 800. The preferences circuit 902 may receive user
preferences from the user device 900 and updated the stored
preferences accordingly. The preferences may include: a selection
of the error metric to be calculated, a time horizon of the error
metric, a weighting structure of the error metric, statistical
properties of the error metric to be calculated, conditions/limits
that trigger an update (e.g., upper control limit, lower control
limit, control chart), etc. Various possible selections for such
preferences are described in detail below. Accordingly, it should
be understood that the update scheduler circuit 800 is highly
customizable to match the needs of various building systems and to
allow for online adjustments as desired by a user.
The error metric calculation circuit 904 is configured to receive
historical predictions and actual data from the database 810 and
calculate and error metric that characterizes the error between the
historical predictions for a past time period and the actual data
for that past time period. The error metric thereby characterizes
the accuracy of the prediction model used to generate the
historical predictions.
The particular error metric used by the error metric calculation
circuit 904 is defined by the preferences circuit 902. In some
embodiments, the error metric calculation circuit 904 calculates a
coefficient of variation weighted mean absolute prediction error
(CVWMAPE). The CVWMAPE is a metric calculated for every instant k
at which a prediction over the horizon is performed as shown in
(Eq. 1) below. The CVWMAPE is a scaled version of the weighted mean
absolute prediction error (WMAPE) shown in (Eq. 2). In some
embodiments, the error metric calculation circuit 904 calculates
the WMAPE. The CVMAPE metric shows the exponentially weighted
average of the absolute prediction error at each instant for which
a prediction of the load or rate is generated:
.times..times..function..times..times..function..times..times..function..-
times..function..times..times..function..times. ##EQU00007## where
L.sub.i is the actual value of the load or rate at instant i over
the horizon, {circumflex over (L)}.sub.i is the predicted value of
the load or rate at instant i over the horizon, h is the length of
the horizon, and w(i) is a weighting function. The weighting
function may be defined by the preferences circuit 902 and may be
selectable by a user via the user device 900. For example, in some
cases the weighting function may be defined or selected as
w(i)=e.sup.-i/h. The CVMAPE or WMAPE metric is calculated (updated)
for every time step k for which a prediction is made.
In some embodiments, the error metric calculation circuit 904
calculates a smoothed (filtered) current prediction error. The
smoothed current prediction error is another metric for evaluating
the prediction of loads and rates. In the notation defined above,
the smoothed current prediction error e.sub.i is a smoothing of a
current prediction error r.sub.i=L.sub.i-{circumflex over
(L)}.sub.i and is defined as
e.sub.i=.lamda.*e.sub.i-1+(1-.lamda.)*r.sub.i-1, where
0<.lamda.<1. The factor .lamda. may be referred to as the
forgetting factor. Such a smoothing or filtering based on a
forgetting factor may also be applied to the CVWMAPE or the
WMAPE.
The error metric calculation circuit 904 thereby calculates an
error metric over a preceding amount of time. The error metric
thereby changes with time, such that a new value of the error
metric may be calculated at each time step. The error metric
calculation circuit 904 provides the error metric to the metric
properties circuit 906.
The metric properties circuit 906 is configured to determine one or
more statistical properties of the error metric. For example, the
metric properties circuit 906 may determine a control region for
the error metric by applying statistical analysis to the error
metric over time. In some embodiments, the metric properties
circuit 906 generates a statistically-defined upper limit and a
statistically-defined lower limit on the value of the error metric.
Such limits may be provided to the condition monitoring circuit
908. Because the error metric changes over time, the statistical
properties (i.e., the values thereof) of the error metric also
change over time. In some embodiments, the metric properties
circuit 906 is configured to update the statistical property in
response to an update to the prediction model by the models update
circuit 802 (e.g., such that the updated statistical property is
based on the updated prediction model). In such a case, the upper
limit and/or lower limit may be updated based on the updated
statistical property (i.e., based on the updated prediction
model).
As one example, the metric properties circuit 906 calculate the
statistical property based on a standard deviation. For example,
when the models are generated (e.g., as a regression model), there
can be residuals from the fit. In such a case, the standard
deviation of the residuals may be used to define the upper limit
and/or lower limit. For example, the upper limit may be defined
using +2*.sigma. and the lower limit may be defined using
-2*.sigma.. It should be understood that the present disclosure
contemplates many such examples.
The condition monitoring circuit 908 is configured to monitor the
error metric, determine when a trigger condition is met, and in
response to determining that the trigger limit is met, generate a
command to update the prediction model. The trigger condition may
be defined based on a control region, where the trigger condition
is met when the value of the error metric is outside of the control
region (i.e., greater than an upper limit or less than a lower
limit). In some embodiments, the trigger condition is met when the
value of the error metric is outside of the control region for at
least a threshold duration of time. The trigger condition may
represent an error in the prediction model beyond an acceptable
level.
The upper and lower limits and/or threshold duration may be defined
statistically and calculated by the metric properties circuit 906
and/or may be defined as user preferences via the user device 900
and stored in the preferences circuit 902. In a case where the
trigger condition (e.g., limit(s) of a control region) is defined
as a statistical property of the error metric, the trigger
condition may be updated to remain in accordance with changes in
the statistical property of the error metric over time.
Accordingly, the trigger condition may be automatically adjusted
overtime to track behavior of the error metric.
When the condition monitoring circuit 908 determines that the
trigger condition is met, indicating that the prediction model has
deviated from an acceptable level of accuracy, the condition
monitoring circuit 908 generates an update command and provides the
update command to the models update circuit 802. The update command
causes the models update circuit 802 to update the corresponding
prediction model(s). The corresponding prediction model(s) may then
be updated and used to generate control signals to control the
equipment 812.
In some embodiments, updated scheduler circuit 800 is configured to
generate a graphical user interface that includes an indication of
the error metric and the trigger conditions. For example, the
graphical interface may include a graph showing the error metric
and the trigger condition for each time step in a displayed time
period. The graphical user interface may be provided to the user
device 900 and displayed on the user device 900. A user may thereby
be able to view the error metric and the trigger condition and
monitor the behavior of the update scheduler circuit 800 over time,
for example to facilitate a user in changing various preferences or
taking other actions relating to the operation of the updated
scheduler circuit 800 or the equipment 812.
It should be understood that the calculation of the error metric,
the determination of statistical properties relating to the error
metric, and the trigger conditions are all highly configurable and
customizable based on user preferences input via the user device
900. The update scheduler circuit 800 may have a flexible
architecture that facilitates online adjustments to the
calculations executed thereby. For example, the weighting in the
calculation of the CVWMAPE may be defined or selected by user
input. As another example, a user may be allowed to selected
between use of the CVWMAPE and the smoothed current prediction
error for online operation of updated scheduler circuit 800. The
value of the forgetting factor A may also be selected by a user.
Various other adjustments may be made to tailor this approach to
various building systems and various user preferences.
Configuration of Exemplary Embodiments
Although the figures show a specific order of method steps, the
order of the steps may differ from what is depicted. Also two or
more steps can be performed concurrently or with partial
concurrence. Such variation will depend on the software and
hardware systems chosen and on designer choice. All such variations
are within the scope of the disclosure. Likewise, software
implementations could be accomplished with standard programming
techniques with rule based logic and other logic to accomplish the
various connection steps, calculation steps, processing steps,
comparison steps, and decision steps.
The construction and arrangement of the systems and methods as
shown in the various exemplary embodiments are illustrative only.
Although only a few embodiments have been described in detail in
this disclosure, many modifications are possible (e.g., variations
in sizes, dimensions, structures, shapes and proportions of the
various elements, values of parameters, mounting arrangements, use
of materials, colors, orientations, etc.). For example, the
position of elements can be reversed or otherwise varied and the
nature or number of discrete elements or positions can be altered
or varied. Accordingly, all such modifications are intended to be
included within the scope of the present disclosure. The order or
sequence of any process or method steps can be varied or
re-sequenced according to alternative embodiments. Other
substitutions, modifications, changes, and omissions can be made in
the design, operating conditions and arrangement of the exemplary
embodiments without departing from the scope of the present
disclosure.
As used herein, the term "circuit" may include hardware structured
to execute the functions described herein. In some embodiments,
each respective "circuit" may include machine-readable media for
configuring the hardware to execute the functions described herein.
The circuit may be embodied as one or more circuitry components
including, but not limited to, processing circuitry, network
interfaces, peripheral devices, input devices, output devices,
sensors, etc. In some embodiments, a circuit may take the form of
one or more analog circuits, electronic circuits (e.g., integrated
circuits (IC), discrete circuits, system on a chip (SOCs) circuits,
etc.), telecommunication circuits, hybrid circuits, and any other
type of "circuit." In this regard, the "circuit" may include any
type of component for accomplishing or facilitating achievement of
the operations described herein. For example, a circuit as
described herein may include one or more transistors, logic gates
(e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR, etc.), resistors,
multiplexers, registers, capacitors, inductors, diodes, wiring, and
so on).
The "circuit" may also include one or more processors communicably
coupled to one or more memory or memory devices. In this regard,
the one or more processors may execute instructions stored in the
memory or may execute instructions otherwise accessible to the one
or more processors. In some embodiments, the one or more processors
may be embodied in various ways. The one or more processors may be
constructed in a manner sufficient to perform at least the
operations described herein. In some embodiments, the one or more
processors may be shared by multiple circuits (e.g., circuit A and
circuit B may comprise or otherwise share the same processor which,
in some example embodiments, may execute instructions stored, or
otherwise accessed, via different areas of memory). Alternatively
or additionally, the one or more processors may be structured to
perform or otherwise execute certain operations independent of one
or more co-processors. In other example embodiments, two or more
processors may be coupled via a bus to enable independent,
parallel, pipelined, or multi-threaded instruction execution. Each
processor may be implemented as one or more general-purpose
processors, application specific integrated circuits (ASICs), field
programmable gate arrays (FPGAs), digital signal processors (DSPs),
or other suitable electronic data processing components structured
to execute instructions provided by memory. The one or more
processors may take the form of a single core processor, multi-core
processor (e.g., a dual core processor, triple core processor, quad
core processor, etc.), microprocessor, etc. In some embodiments,
the one or more processors may be external to the apparatus, for
example the one or more processors may be a remote processor (e.g.,
a cloud based processor). Alternatively or additionally, the one or
more processors may be internal and/or local to the apparatus. In
this regard, a given circuit or components thereof may be disposed
locally (e.g., as part of a local server, a local computing system,
etc.) or remotely (e.g., as part of a remote server such as a cloud
based server). To that end, a "circuit" as described herein may
include components that are distributed across one or more
locations. The present disclosure contemplates methods, systems and
program products on any machine-readable media for accomplishing
various operations. The embodiments of the present disclosure can
be implemented using existing computer processors, or by a special
purpose computer processor for an appropriate system, incorporated
for this or another purpose, or by a hardwired system. Embodiments
within the scope of the present disclosure include program products
comprising machine-readable media for carrying or having
machine-executable instructions or data structures stored thereon.
Such machine-readable media can be any available media that can be
accessed by a general purpose or special purpose computer or other
machine with a processor. By way of example, such machine-readable
media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical
disk storage, magnetic disk storage or other magnetic storage
devices, or any other medium which can be used to carry or store
desired program code in the form of machine-executable instructions
or data structures and which can be accessed by a general purpose
or special purpose computer or other machine with a processor.
Combinations of the above are also included within the scope of
machine-readable media. Machine-executable instructions include,
for example, instructions and data which cause a general purpose
computer, special purpose computer, or special purpose processing
machines to perform a certain function or group of functions.
* * * * *